A finite-sample certificate for which fragments a retrieval corpus should keep, valid even after data-dependent selection
When a retrieval-augmented generation corpus must be trimmed under a storage or annotation budget, how much query coverage the chosen fragments lose relative to the best feasible set has lacked a finite-sample guarantee that survives data-dependent selection. This work separates selection from auditing: a pilot picks the set, screens competitors and builds a comparison network, then an independent audit acquires relevance labels with probabilities constant within observable strata and yields a simultaneous bound on all retained coverage contrasts, with simulations confirming the nominal level.